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Iulie 22, 2017

In my article published in Mankind Quarterly (Cretan, 2016), I predicted that selection for high intelligence must parallel selection against psychiatric disorders: „Apart from intelligence, a good indicator for brain functioning in a population is the prevalence of mental disorders. A study of the prevalence of psychiatric disorders in the USA, in the non-institutionalized population aged 15-54, found that nearly 50% of respondents reported at least one lifetime disorder and 30% reported at least one 12-month disorder. More than half of lifetime disorders occurred in the 14% of the population who had a history of at least three comorbid disorders (Kessler et al., 1994). Even if the 14% (of those with disorders) representing alcohol-related pathology are excluded, the prevalence remains very high. A meta-analysis of 27 studies with a combined sample size of 150,000 subjects aged 18 to 65, from 16 European countries, found that 27% had been affected by at least one mental disorder in the last 12 months (Wittchen & Jacobi, 2005). Personality disorders, being life-long, have an over 9% prevalence in the US population (Lezenweger, 2007). However, a Finnish study reveals a total prevalence of mental disorders of only 17.4% (Lethinen et al., 1990). The heritability for each of these disorders is at least 40% (Burmeister, McInnis & Zöllner, 2008).
Should natural selection have managed to increase intelligence in the last 10,000 years, then it should have also decreased the prevalence of mental disorders in the same period. In this case, the Mesolithic population must have had a high rate of psychiatric disorders in addition to lower intelligence. However, it is the Finns who, being genetically closest to European Mesolithic hunter-gatherers and most distant from the Neolithic farmers, were the last to abandon the hunter-gatherer lifestyle and had the least time available to adapt to modern life, who have the lowest prevalence of mental disorders.”

Although, the GWAS on IQ of Sniekers (2017) found a negative genetic correlation between intelligence and Alzheimer’s disease (-0.36), depressive symptoms (-0.27), ADHD (-0.27), schizophrenia (-0.20), anxiety (-0.19), neuroticism (-0.19), insomnia (-0.14), major depressive disorder (-0.11), Parkinson’s disease (-0.01) and bipolar disorder (-0.01). Positive correlations were found only with ASD (0.21) and anorexia nervosa (0.08).

At least concerning schizophrenia, my prediction seems be correct. Ohi (2017) published the frequencies of 122 SNP that favor schizophrenia in continental populations of 1000 GENOMES. I calculated a risc score on schizophrenia (POLY_SCZ) for these populations, and I found a negative correlation between POLY_SCZ and measured IQ. (I added the frequencies of each allele in a population and I divided by 122, the number of SNP. I obtained an average frequency of a SNP in each population.) Here are the results:

AFR=0.3040 (IQ=69)

SAS=0.2926 (IQ=82)

AMR=0.2915 (IQ=85)

EUR=0.2886 (IQ=99)

EAS=0.2776 (IQ=104)

The average frequency in populations of an IQ-increasing SNP is also around 0.3, for the 15 lead SNP of Sniekers (Piffer, 2017):





For comparison, here are POLY_IQ on 15 SNP (Piffer, 2017; Sniekers, 2017) and POLY_EDU on 161 SNP (Piffer, 2016; Okbay, 2016):

AFR – 0.4714 – 0.4804

SAS – 0.4439 – 0.5038

AMR – 0.4515 – 0.4890

EUR – 0.4627 – 0.5102

EAS – 0.5129 – 0.5234

We can observe that POLY_SCZ predicts better the measured IQ than POLY_IQ and POLY_EDU.

Only my scores, based on POLY_IQ/EDU and the number of rare alleles per genome, predicts the measured IQ of populations (excepting AMR) like POLY_SCZ (

Srinivasan (2016) found that Neanderthals had a lower „POLY_SCZ” than today humans. This fact suggests that Neanderthals could have a higher intelligence than today humans. Also, Cro-Magnons, that had higher amounts of Neanderthal admixture than today humans (Fu, 2016), could have a higher genotypic intelligence than today humans.

But, concerning today populations, the amount of Neanderthal admixture (Sankararaman, 2016) does not parallel the POLY_SCZ and the measured IQ of continental populations. It means that after the admixture of humans and Neanderthals there were different selection pressures on intelligence and on psychiatric disorder risk of continental populations. Although, Wong (2017) observed variation in allelic differentiation between populations at tissue-specific expression quantitative trait loci (eQTL), with greatest effects found for genes expressed in a region of the brain that has been linked to schizophrenia and bipolar disorder. Consistent with this, genome-wide association study regions also showed high levels of population differentiation for these diseases. The most parsimonious explanation for this high differentiation found by Wong (2017) and by different GWAS is a relaxed selection on intelligence and psychiatric disorder risk, at least for some of continental populations. This is in line with the study of Racimo (2017), that found selection on educational attainment only for East Asians and only before Holocene period. Also, POLY_SCZ is part of mutational load. The mutational load is due 90% of common polymorphism (Henn, 2016). Furthermore, the mutational load increases with distance from Africa (Henn, 2016). But East Asians do not have the the highest POLY_SCZ, but the lowest. Hence, in East Asians the selection against psychiatric disorders (and favoring high intelligence) was the strongest between all continental populations.

In my opinion, all these facts represent new evidence for the decrease of the genotypic intelligence of humans since Palaeolithic period.



Cretan, C. (2016) Was the Cro-Magnon the Most Intelligent Modern Human? MANKIND QUARTERLY 57:2 158-195

Cretan, C. (2017)

Fu, Q. et al (2016) The genetic history of Ice Age Europe. Nature 534: 200–205 doi:10.1038/nature17993

Henn, B. et al (2016) Distance from su-Saharan Africa predicts mutational load in diverse human genomes. PNAS 113(4): E440-449 doi:  10.1073/pnas.1510805112

Ohi, K. et al (2017) Variability of 128 schizophrenia-associated gene variants
across distinct ethnic populations. Transl Psychiatry 7, e988; doi:10.1038/tp.2016.260

Okbay, A. et al. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533: 539-542.

Piffer, D. (2016). Polygenic selection on educational attainment: a replication.

Piffer, D. (2017)  2017 Intelligence GWAS: Group-level polygenic scores

Sniekers, S. et al (2017) Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics doi:10.1038/ng.3869

Racimo, F. et al (2017) Detecting polygenic adaptation in admixture graphs. bioRxiv doi:

Sankararaman, S. et al (2016) The Combined Landscape of Denisovan and Neanderthal Ancestry in Present-Day Humans. Current Biology,

Srinivasan, S. et al (2016) Genetic Markers of Human Evolution Are
Enriched in Schizophrenia. Biological Psychiatry 80:284–292

Wong, E.S. & Powel, J.E. (2017) Allelic differentiation of complex trait loci across human populations. bioRxiv doi: .





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